R Notebook: estimation of gas costs using the measure marginal method; for Gas Cost Estimator; for a single given env
In this script we conduct the estimation for the
measure_marginal approach for a single given env =
nethermind.
PROGRAMS=pg_marginal_full5_c50_step1_shuffle SAMPLESIZE=50 NSAMPLES=4.
Expected a result file nethermind_pg_marginal_full5_c50_step1_shuffle_size_10.csv.
programs = read.csv(paste("stage3/", program_set_codename, ".csv", sep=""))
results = load_data_set(env, program_set_codename, measurement_codename)
# besu may have additional columns with gc stats
results = results[, c("program_id", "sample_id", "run_id", "measure_total_time_ns", "measure_total_timer_time_ns", "env")]
# TODO geth short-circuits zero length programs, resulting in zero timing somehow. Drop these more elegantly, not based on measure_total_time_ns
results = results[which(results$measure_total_time_ns != 0), ]
all_envs = c(env)measurements = sqldf("SELECT opcode, op_count, sample_id, run_id, measure_total_time_ns, env, results.program_id
FROM results
INNER JOIN
programs ON(results.program_id = programs.program_id)")
measurements$opcode = factor(measurements$opcode, levels=unique(programs$opcode))
head(measurements)## opcode op_count sample_id run_id measure_total_time_ns env program_id
## 1 ADD 27 0 1 10762.32 nethermind ADD_27
## 2 ADD 27 0 2 11183.77 nethermind ADD_27
## 3 ADD 27 0 3 10759.05 nethermind ADD_27
## 4 ADD 27 0 4 11155.19 nethermind ADD_27
## 5 ADD 27 0 5 10926.58 nethermind ADD_27
## 6 ADD 27 0 6 11877.04 nethermind ADD_27Switch removed_outliers to FALSE to see the
comparison.
boxplot(measurements[which(measurements$env == env), 'measure_total_time_ns'] ~ measurements[which(measurements$env == env), 'opcode'], las=2, outline=TRUE, log='y', main=paste(env, 'all'))
if (removed_outliers) {
measurements = remove_compare_outliers(measurements, 'measure_total_time_ns', all_envs)
}
# For a subset of the `measurements` data frame, fits a bimodal distribution model and corrects the
# data by bringing the "top-mode" cluster down to the "bottom-mode" cluster.
correct_bimodal <- function(df) {
mix_model = normalmixEM(df$measure_total_time_ns)
print(summary(mix_model))
plot(mix_model,which=2)
mode_distance = abs(mix_model$mu[2] - mix_model$mu[1])
mode_midpoint = (mix_model$mu[2] + mix_model$mu[1]) / 2
over_threshold = which(df$measure_total_time_ns > mode_midpoint)
df[over_threshold, "measure_total_time_ns"] = df[over_threshold, "measure_total_time_ns"] - mode_distance
return(df)
}
# Performs the `measure_marginal` estimation procedure for a given slice of the data.
# Prints the diagnostics and plots the models.
compute_all <- function(opcode, env, plots, bimodal_opcodes, use_median) {
if (missing(bimodal_opcodes)) {
bimodal_opcodes = c()
}
if (missing(plots)) {
plots = "scatter"
}
if (missing(use_median)) {
use_median = FALSE
}
print(c(opcode, env))
df = measurements[which(measurements$opcode==opcode & measurements$env==env),]
if (opcode %in% bimodal_opcodes) {
par(mfrow=c(1,2))
boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
title(main=paste(env, opcode))
# correct_bimodal plots the second plot inside
df = correct_bimodal(df)
}
if (use_median) {
f = median
} else {
f = mean
}
df_mean = aggregate(measure_total_time_ns ~ op_count * env, df, f)
model_mean = lm(measure_total_time_ns ~ op_count, data=df_mean)
print(summary(model_mean))
slope = model_mean$coefficients[['op_count']]
stderr = summary(model_mean)$coefficients['op_count','Std. Error']
if (plots == "scatter" | plots == "all") {
par(mfrow=c(1,1))
boxplot(measure_total_time_ns ~ op_count, data=df, las=2, outline=removed_outliers)
rounded_slope = round(slope, 3)
rounded_p = round(summary(model_mean)$coefficients['op_count','Pr(>|t|)'], 3)
rounded_stderr = round(stderr, 3)
title(main=paste(env, opcode, rounded_slope, "p_value:", rounded_p, "StdErr:", rounded_stderr))
abline(model_mean, col="red")
}
if (plots == "diagnostics" | plots == "all") {
par(mfrow=c(2,2))
plot(model_mean)
}
list("slope" = slope, "stderr" = stderr)
}
extract_opcodes <- function() {
unique(measurements$opcode)
}all_opcodes = extract_opcodes()
# initialize the data frame to hold the results
estimates = data.frame(matrix(ncol = 4, nrow = 0))
colnames(estimates) <- c('op', 'estimate_marginal_ns', 'estimate_marginal_ns_stderr', 'env')Every sample starts with a fresh evm instance. We investigate whether the results may depend on the time from evm start - related to run_id. To avoid being overrun by the number of images, all op_count for a given run_id are are placed, so values are not centered. That should not an issue.
for (opcode in all_opcodes) {
boxplot(measure_total_time_ns~run_id,data=measurements[measurements$opcode == opcode,], main=opcode)
}
Now we can investigate the linear regressions.
if (env == 'evmone') {
bimodals = all_opcodes[which(grepl("PUSH", all_opcodes) & all_opcodes != "PUSH1" | all_opcodes == "JUMP")]
} else {
bimodals = c()
}
for (opcode in all_opcodes) {
estimate = compute_all(opcode=opcode, env=env, use_median=TRUE, bimodal_opcodes=bimodals, plots='all')
estimates[nrow(estimates) + 1, ] = c(opcode, estimate, env)
}## [1] "ADD" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -303.35 -117.39 -41.44 86.40 373.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10500.745 45.413 231.23 < 0.0000000000000002 ***
## op_count 17.541 1.565 11.21 0.00000000000000402 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 164.5 on 49 degrees of freedom
## Multiple R-squared: 0.7193, Adjusted R-squared: 0.7136
## F-statistic: 125.6 on 1 and 49 DF, p-value: 0.000000000000004021
## [1] "MUL" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -345.37 -96.56 9.06 125.95 352.66
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10426.802 46.325 225.08 <0.0000000000000002 ***
## op_count 47.157 1.597 29.53 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 167.9 on 49 degrees of freedom
## Multiple R-squared: 0.9468, Adjusted R-squared: 0.9457
## F-statistic: 872.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SUB" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -335.2 -131.5 -27.5 135.2 326.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10482.212 46.956 223.24 < 0.0000000000000002 ***
## op_count 19.642 1.619 12.14 0.000000000000000223 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 170.1 on 49 degrees of freedom
## Multiple R-squared: 0.7504, Adjusted R-squared: 0.7453
## F-statistic: 147.3 on 1 and 49 DF, p-value: 0.0000000000000002231
## [1] "DIV" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -334.62 -124.77 14.53 101.63 665.94
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10349.40 48.16 214.90 <0.0000000000000002 ***
## op_count 24.92 1.66 15.01 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 174.5 on 49 degrees of freedom
## Multiple R-squared: 0.8214, Adjusted R-squared: 0.8178
## F-statistic: 225.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SDIV" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -232.38 -70.69 -13.65 47.92 327.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10507.029 35.734 294.03 <0.0000000000000002 ***
## op_count 32.929 1.232 26.73 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 129.5 on 49 degrees of freedom
## Multiple R-squared: 0.9358, Adjusted R-squared: 0.9345
## F-statistic: 714.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MOD" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -295.08 -170.60 28.06 109.81 457.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10445.468 53.044 196.92 <0.0000000000000002 ***
## op_count 22.625 1.828 12.37 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 192.2 on 49 degrees of freedom
## Multiple R-squared: 0.7576, Adjusted R-squared: 0.7526
## F-statistic: 153.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SMOD" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -328.85 -133.22 -26.65 133.40 544.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10507.431 51.741 203.08 <0.0000000000000002 ***
## op_count 35.940 1.783 20.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 187.5 on 49 degrees of freedom
## Multiple R-squared: 0.8923, Adjusted R-squared: 0.8901
## F-statistic: 406.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ADDMOD" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -349.65 -66.42 -1.88 100.27 368.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10494.888 42.404 247.50 <0.0000000000000002 ***
## op_count 38.599 1.462 26.41 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 153.6 on 49 degrees of freedom
## Multiple R-squared: 0.9344, Adjusted R-squared: 0.933
## F-statistic: 697.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MULMOD" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -536.99 -187.22 -19.85 170.99 631.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10666.68 68.19 156.43 <0.0000000000000002 ***
## op_count 106.59 2.35 45.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 247.1 on 49 degrees of freedom
## Multiple R-squared: 0.9767, Adjusted R-squared: 0.9763
## F-statistic: 2057 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "EXP" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -817.7 -153.0 -40.1 169.9 668.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10971.84 76.60 143.24 <0.0000000000000002 ***
## op_count 204.52 2.64 77.46 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 277.5 on 49 degrees of freedom
## Multiple R-squared: 0.9919, Adjusted R-squared: 0.9917
## F-statistic: 6001 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SIGNEXTEND" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -457.91 -144.19 -8.03 92.86 502.23
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10538.039 53.581 196.7 <0.0000000000000002 ***
## op_count 27.700 1.847 15.0 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 194.1 on 49 degrees of freedom
## Multiple R-squared: 0.8211, Adjusted R-squared: 0.8175
## F-statistic: 224.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "LT" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -521.57 -203.88 -71.98 83.01 2407.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10201.935 120.343 84.774 < 0.0000000000000002 ***
## op_count 26.201 4.148 6.316 0.000000076 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 436 on 49 degrees of freedom
## Multiple R-squared: 0.4488, Adjusted R-squared: 0.4375
## F-statistic: 39.9 on 1 and 49 DF, p-value: 0.00000007603
## [1] "GT" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -450.60 -110.12 -7.09 97.18 336.57
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10374.358 45.349 228.77 < 0.0000000000000002 ***
## op_count 16.927 1.563 10.83 0.0000000000000134 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 164.3 on 49 degrees of freedom
## Multiple R-squared: 0.7053, Adjusted R-squared: 0.6993
## F-statistic: 117.3 on 1 and 49 DF, p-value: 0.0000000000000134
## [1] "SLT" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -422.23 -100.49 -15.85 97.77 418.59
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10407.895 47.426 219.46 <0.0000000000000002 ***
## op_count 22.966 1.635 14.05 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 171.8 on 49 degrees of freedom
## Multiple R-squared: 0.8011, Adjusted R-squared: 0.7971
## F-statistic: 197.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SGT" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -334.8 -128.8 11.6 108.2 525.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10394.034 48.682 213.51 <0.0000000000000002 ***
## op_count 22.962 1.678 13.68 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 176.4 on 49 degrees of freedom
## Multiple R-squared: 0.7926, Adjusted R-squared: 0.7884
## F-statistic: 187.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "EQ" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -310.201 -61.999 5.632 71.733 276.373
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10316.83 33.95 303.90 <0.0000000000000002 ***
## op_count 15.76 1.17 13.47 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 123 on 49 degrees of freedom
## Multiple R-squared: 0.7873, Adjusted R-squared: 0.783
## F-statistic: 181.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ISZERO" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -209.21 -105.20 -5.64 67.77 395.72
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10369.90 38.88 266.709 < 0.0000000000000002 ***
## op_count 11.02 1.34 8.222 0.0000000000875 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 140.9 on 49 degrees of freedom
## Multiple R-squared: 0.5798, Adjusted R-squared: 0.5712
## F-statistic: 67.61 on 1 and 49 DF, p-value: 0.00000000008754
## [1] "AND" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -387.69 -94.85 -23.23 76.32 518.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10381.419 43.121 240.8 <0.0000000000000002 ***
## op_count 34.486 1.486 23.2 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 156.2 on 49 degrees of freedom
## Multiple R-squared: 0.9166, Adjusted R-squared: 0.9149
## F-statistic: 538.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "OR" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -238.03 -103.89 -11.26 84.55 408.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10458.68 41.20 253.88 <0.0000000000000002 ***
## op_count 32.12 1.42 22.62 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 149.3 on 49 degrees of freedom
## Multiple R-squared: 0.9126, Adjusted R-squared: 0.9108
## F-statistic: 511.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "XOR" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -393.92 -101.86 -24.74 94.41 369.54
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10408.809 43.603 238.72 <0.0000000000000002 ***
## op_count 33.504 1.503 22.29 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 158 on 49 degrees of freedom
## Multiple R-squared: 0.9102, Adjusted R-squared: 0.9084
## F-statistic: 496.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "NOT" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -364.74 -119.21 23.24 106.02 334.46
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10364.41 41.77 248.16 <0.0000000000000002 ***
## op_count 25.02 1.44 17.38 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 151.3 on 49 degrees of freedom
## Multiple R-squared: 0.8605, Adjusted R-squared: 0.8576
## F-statistic: 302.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "BYTE" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -328.57 -112.69 -22.09 74.55 447.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10326.57 47.85 215.79 < 0.0000000000000002 ***
## op_count 19.12 1.65 11.59 0.0000000000000012 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 173.4 on 49 degrees of freedom
## Multiple R-squared: 0.7328, Adjusted R-squared: 0.7273
## F-statistic: 134.3 on 1 and 49 DF, p-value: 0.000000000000001198
## [1] "SHL" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -476.43 -108.27 -2.58 103.93 534.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10499.140 51.989 201.95 <0.0000000000000002 ***
## op_count 34.549 1.792 19.28 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 188.4 on 49 degrees of freedom
## Multiple R-squared: 0.8835, Adjusted R-squared: 0.8811
## F-statistic: 371.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SHR" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -580.88 -117.54 16.89 143.30 352.96
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10579.521 47.963 220.58 <0.0000000000000002 ***
## op_count 29.517 1.653 17.85 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 173.8 on 49 degrees of freedom
## Multiple R-squared: 0.8668, Adjusted R-squared: 0.864
## F-statistic: 318.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SAR" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -328.09 -121.00 13.47 109.79 423.67
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10473.428 48.353 216.60 <0.0000000000000002 ***
## op_count 36.747 1.667 22.05 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 175.2 on 49 degrees of freedom
## Multiple R-squared: 0.9084, Adjusted R-squared: 0.9066
## F-statistic: 486.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ADDRESS" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -275.622 -69.146 -9.657 72.780 303.130
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8213.950 32.976 249.09 <0.0000000000000002 ***
## op_count 16.321 1.137 14.36 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 119.5 on 49 degrees of freedom
## Multiple R-squared: 0.808, Adjusted R-squared: 0.804
## F-statistic: 206.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "ORIGIN" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -220.04 -69.56 -30.78 55.70 247.01
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8213.234 30.414 270.05 <0.0000000000000002 ***
## op_count 16.051 1.048 15.31 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 110.2 on 49 degrees of freedom
## Multiple R-squared: 0.8271, Adjusted R-squared: 0.8236
## F-statistic: 234.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLER" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -229.71 -90.17 1.79 95.10 352.94
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8260.182 34.994 236.04 <0.0000000000000002 ***
## op_count 14.897 1.206 12.35 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 126.8 on 49 degrees of freedom
## Multiple R-squared: 0.7569, Adjusted R-squared: 0.7519
## F-statistic: 152.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLVALUE" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -266.42 -112.13 29.91 120.31 290.73
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8213.245 40.097 204.836 < 0.0000000000000002 ***
## op_count 10.012 1.382 7.244 0.00000000278 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 145.3 on 49 degrees of freedom
## Multiple R-squared: 0.5171, Adjusted R-squared: 0.5073
## F-statistic: 52.47 on 1 and 49 DF, p-value: 0.000000002783
## [1] "CALLDATALOAD" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -305.50 -130.78 -8.54 99.03 492.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12052.052 50.312 239.54 <0.0000000000000002 ***
## op_count 22.426 1.734 12.93 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 182.3 on 49 degrees of freedom
## Multiple R-squared: 0.7734, Adjusted R-squared: 0.7688
## F-statistic: 167.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CALLDATASIZE" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -402.98 -94.89 8.07 75.94 399.85
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8269.891 38.185 216.574 < 0.0000000000000002 ***
## op_count 12.248 1.316 9.305 0.00000000000208 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 138.4 on 49 degrees of freedom
## Multiple R-squared: 0.6386, Adjusted R-squared: 0.6312
## F-statistic: 86.59 on 1 and 49 DF, p-value: 0.000000000002077
## [1] "CALLDATACOPY" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -361.95 -141.54 -32.63 86.76 1215.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11405.283 72.499 157.32 <0.0000000000000002 ***
## op_count 103.161 2.499 41.28 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 262.7 on 49 degrees of freedom
## Multiple R-squared: 0.9721, Adjusted R-squared: 0.9715
## F-statistic: 1704 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CODESIZE" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -236.18 -71.54 1.54 76.56 228.79
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8268.716 29.705 278.36 < 0.0000000000000002 ***
## op_count 12.097 1.024 11.81 0.000000000000000598 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 107.6 on 49 degrees of freedom
## Multiple R-squared: 0.7402, Adjusted R-squared: 0.7349
## F-statistic: 139.6 on 1 and 49 DF, p-value: 0.0000000000000005981
## [1] "CODECOPY" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -414.75 -204.99 -36.65 135.43 745.60
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11311.926 74.481 151.88 <0.0000000000000002 ***
## op_count 104.331 2.567 40.64 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 269.9 on 49 degrees of freedom
## Multiple R-squared: 0.9712, Adjusted R-squared: 0.9706
## F-statistic: 1651 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GASPRICE" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -290.669 -64.089 -5.319 78.323 205.128
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8225.685 29.542 278.44 < 0.0000000000000002 ***
## op_count 11.392 1.018 11.19 0.00000000000000426 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 107 on 49 degrees of freedom
## Multiple R-squared: 0.7187, Adjusted R-squared: 0.7129
## F-statistic: 125.2 on 1 and 49 DF, p-value: 0.00000000000000426
## [1] "RETURNDATASIZE" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -182.90 -93.33 14.97 73.70 258.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8340.834 31.810 262.21 <0.0000000000000002 ***
## op_count 18.184 1.096 16.58 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 115.3 on 49 degrees of freedom
## Multiple R-squared: 0.8488, Adjusted R-squared: 0.8457
## F-statistic: 275 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "RETURNDATACOPY" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -720.40 -222.14 -86.43 165.00 1545.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20266.751 116.488 173.98 <0.0000000000000002 ***
## op_count 108.967 4.015 27.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 422.1 on 49 degrees of freedom
## Multiple R-squared: 0.9376, Adjusted R-squared: 0.9363
## F-statistic: 736.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "COINBASE" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -214.036 -68.886 4.526 90.659 211.913
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8269.085 29.051 284.64 <0.0000000000000002 ***
## op_count 18.169 1.001 18.14 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 105.3 on 49 degrees of freedom
## Multiple R-squared: 0.8704, Adjusted R-squared: 0.8678
## F-statistic: 329.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "TIMESTAMP" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -265.166 -98.328 -2.111 84.028 303.058
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8318.416 35.731 232.808 < 0.0000000000000002 ***
## op_count 11.874 1.232 9.641 0.000000000000667 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 129.5 on 49 degrees of freedom
## Multiple R-squared: 0.6548, Adjusted R-squared: 0.6478
## F-statistic: 92.95 on 1 and 49 DF, p-value: 0.0000000000006673
## [1] "NUMBER" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -306.23 -95.94 -17.79 69.50 471.80
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8306.29 41.48 200.271 < 0.0000000000000002 ***
## op_count 12.26 1.43 8.578 0.0000000000253 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 150.3 on 49 degrees of freedom
## Multiple R-squared: 0.6003, Adjusted R-squared: 0.5921
## F-statistic: 73.59 on 1 and 49 DF, p-value: 0.00000000002528
## [1] "DIFFICULTY" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -216.31 -69.41 -17.53 78.70 220.46
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8262.477 29.222 282.75 <0.0000000000000002 ***
## op_count 13.520 1.007 13.42 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 105.9 on 49 degrees of freedom
## Multiple R-squared: 0.7862, Adjusted R-squared: 0.7818
## F-statistic: 180.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "GASLIMIT" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -271.038 -55.374 -1.162 60.481 246.127
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8302.3359 28.5684 290.6 <0.0000000000000002 ***
## op_count 13.5920 0.9847 13.8 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 103.5 on 49 degrees of freedom
## Multiple R-squared: 0.7954, Adjusted R-squared: 0.7912
## F-statistic: 190.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "CHAINID" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -362.82 -81.48 -6.27 86.75 302.73
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8299.636 37.903 218.97 <0.0000000000000002 ***
## op_count 19.819 1.306 15.17 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 137.3 on 49 degrees of freedom
## Multiple R-squared: 0.8244, Adjusted R-squared: 0.8209
## F-statistic: 230.1 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "SELFBALANCE" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -257.49 -64.01 -22.33 80.02 321.24
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8351.62 32.49 257.02 <0.0000000000000002 ***
## op_count 35.93 1.12 32.08 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 117.7 on 49 degrees of freedom
## Multiple R-squared: 0.9546, Adjusted R-squared: 0.9536
## F-statistic: 1029 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "POP" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -297.03 -75.49 -26.09 70.72 405.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9223.136 37.975 242.875 < 0.0000000000000002 ***
## op_count 5.022 1.309 3.836 0.000358 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 137.6 on 49 degrees of freedom
## Multiple R-squared: 0.231, Adjusted R-squared: 0.2153
## F-statistic: 14.72 on 1 and 49 DF, p-value: 0.0003575
## [1] "MLOAD" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -286.14 -123.19 -12.35 90.72 449.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12136.460 50.546 240.11 <0.0000000000000002 ***
## op_count 56.158 1.742 32.23 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 183.1 on 49 degrees of freedom
## Multiple R-squared: 0.955, Adjusted R-squared: 0.954
## F-statistic: 1039 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MSTORE" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -361.5 -119.8 -14.3 110.1 560.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11113.086 52.558 211.44 <0.0000000000000002 ***
## op_count 48.596 1.812 26.82 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 190.4 on 49 degrees of freedom
## Multiple R-squared: 0.9362, Adjusted R-squared: 0.9349
## F-statistic: 719.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "MSTORE8" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -304.15 -108.01 -19.53 100.18 662.24
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11020.359 43.878 251.16 <0.0000000000000002 ***
## op_count 51.617 1.512 34.13 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 159 on 49 degrees of freedom
## Multiple R-squared: 0.9596, Adjusted R-squared: 0.9588
## F-statistic: 1165 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "JUMP" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -707.51 -73.16 9.88 77.34 347.46
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9180.319 43.148 212.77 <0.0000000000000002 ***
## op_count 18.152 1.487 12.21 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 156.3 on 49 degrees of freedom
## Multiple R-squared: 0.7525, Adjusted R-squared: 0.7474
## F-statistic: 149 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "JUMPI" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1441.20 -163.74 62.44 146.98 581.04
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 11883.177 80.204 148.161 < 0.0000000000000002 ***
## op_count 27.045 2.765 9.783 0.000000000000415 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 290.6 on 49 degrees of freedom
## Multiple R-squared: 0.6614, Adjusted R-squared: 0.6545
## F-statistic: 95.7 on 1 and 49 DF, p-value: 0.0000000000004152
## [1] "PC" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -209.254 -67.861 -7.477 53.126 247.132
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8258.125 30.694 269.044 < 0.0000000000000002 ***
## op_count 8.610 1.058 8.138 0.000000000118 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 111.2 on 49 degrees of freedom
## Multiple R-squared: 0.5747, Adjusted R-squared: 0.5661
## F-statistic: 66.23 on 1 and 49 DF, p-value: 0.0000000001176
## [1] "MSIZE" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -271.386 -92.027 -0.934 100.864 261.673
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8272.160 35.439 233.42 < 0.0000000000000002 ***
## op_count 13.179 1.222 10.79 0.0000000000000153 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 128.4 on 49 degrees of freedom
## Multiple R-squared: 0.7037, Adjusted R-squared: 0.6977
## F-statistic: 116.4 on 1 and 49 DF, p-value: 0.00000000000001526
## [1] "GAS" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -199.849 -73.219 -7.827 76.941 275.525
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8261.289 30.326 272.42 < 0.0000000000000002 ***
## op_count 11.781 1.045 11.27 0.00000000000000328 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 109.9 on 49 degrees of freedom
## Multiple R-squared: 0.7216, Adjusted R-squared: 0.7159
## F-statistic: 127 on 1 and 49 DF, p-value: 0.000000000000003282
## [1] "JUMPDEST" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -751.01 -76.55 13.58 101.98 315.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 6828.869 43.839 155.77 < 0.0000000000000002 ***
## op_count 8.084 1.511 5.35 0.00000231 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 158.8 on 49 degrees of freedom
## Multiple R-squared: 0.3687, Adjusted R-squared: 0.3558
## F-statistic: 28.62 on 1 and 49 DF, p-value: 0.000002308
## [1] "PUSH1" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -227.38 -75.75 -23.20 82.01 249.22
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8145.283 30.545 266.66 < 0.0000000000000002 ***
## op_count 12.678 1.053 12.04 0.000000000000000297 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 110.7 on 49 degrees of freedom
## Multiple R-squared: 0.7474, Adjusted R-squared: 0.7423
## F-statistic: 145 on 1 and 49 DF, p-value: 0.0000000000000002975
## [1] "PUSH2" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -187.34 -99.05 11.66 76.07 274.45
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8180.48 30.46 268.58 <0.0000000000000002 ***
## op_count 18.63 1.05 17.75 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 110.4 on 49 degrees of freedom
## Multiple R-squared: 0.8654, Adjusted R-squared: 0.8626
## F-statistic: 315 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH3" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -288.72 -79.64 -7.41 56.14 343.18
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8248.276 32.657 252.57 <0.0000000000000002 ***
## op_count 16.361 1.126 14.53 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 118.3 on 49 degrees of freedom
## Multiple R-squared: 0.8117, Adjusted R-squared: 0.8079
## F-statistic: 211.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH4" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -220.812 -76.958 -3.645 77.501 285.331
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8228.45 31.03 265.17 <0.0000000000000002 ***
## op_count 16.79 1.07 15.69 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 112.4 on 49 degrees of freedom
## Multiple R-squared: 0.8341, Adjusted R-squared: 0.8307
## F-statistic: 246.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH5" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -235.44 -79.31 -18.56 84.93 325.04
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8233.263 33.188 248.08 <0.0000000000000002 ***
## op_count 16.651 1.144 14.56 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 120.3 on 49 degrees of freedom
## Multiple R-squared: 0.8122, Adjusted R-squared: 0.8083
## F-statistic: 211.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH6" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -221.079 -82.808 -0.808 91.105 214.682
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8254.485 31.301 263.71 <0.0000000000000002 ***
## op_count 16.342 1.079 15.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 113.4 on 49 degrees of freedom
## Multiple R-squared: 0.824, Adjusted R-squared: 0.8204
## F-statistic: 229.4 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH7" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -312.47 -67.50 -4.30 78.17 346.98
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8221.79 35.97 228.55 <0.0000000000000002 ***
## op_count 17.56 1.24 14.16 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 130.3 on 49 degrees of freedom
## Multiple R-squared: 0.8036, Adjusted R-squared: 0.7996
## F-statistic: 200.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH8" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -215.82 -75.19 26.76 60.26 262.13
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8303.846 31.672 262.2 <0.0000000000000002 ***
## op_count 13.646 1.092 12.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 114.8 on 49 degrees of freedom
## Multiple R-squared: 0.7613, Adjusted R-squared: 0.7564
## F-statistic: 156.2 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH9" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -200.94 -85.96 11.08 72.33 270.50
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8286.566 31.381 264.1 <0.0000000000000002 ***
## op_count 15.250 1.082 14.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 113.7 on 49 degrees of freedom
## Multiple R-squared: 0.8022, Adjusted R-squared: 0.7982
## F-statistic: 198.8 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH10" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -234.07 -83.60 -21.19 72.10 265.35
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8339.396 31.675 263.28 <0.0000000000000002 ***
## op_count 13.348 1.092 12.22 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 114.8 on 49 degrees of freedom
## Multiple R-squared: 0.7531, Adjusted R-squared: 0.7481
## F-statistic: 149.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH11" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -327.35 -89.56 15.57 52.07 281.00
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8266.568 34.069 242.64 <0.0000000000000002 ***
## op_count 15.442 1.174 13.15 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 123.4 on 49 degrees of freedom
## Multiple R-squared: 0.7792, Adjusted R-squared: 0.7747
## F-statistic: 172.9 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH12" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -243.97 -82.43 11.79 94.63 211.04
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8258.327 33.613 245.7 <0.0000000000000002 ***
## op_count 15.642 1.159 13.5 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 121.8 on 49 degrees of freedom
## Multiple R-squared: 0.7881, Adjusted R-squared: 0.7838
## F-statistic: 182.3 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH13" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -317.97 -84.24 -2.37 60.71 295.56
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8309.171 34.197 242.98 < 0.0000000000000002 ***
## op_count 12.758 1.179 10.82 0.0000000000000136 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 123.9 on 49 degrees of freedom
## Multiple R-squared: 0.7051, Adjusted R-squared: 0.6991
## F-statistic: 117.1 on 1 and 49 DF, p-value: 0.00000000000001364
## [1] "PUSH14" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -219.01 -76.68 12.18 66.08 215.39
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8296.915 29.938 277.1 <0.0000000000000002 ***
## op_count 13.514 1.032 13.1 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 108.5 on 49 degrees of freedom
## Multiple R-squared: 0.7778, Adjusted R-squared: 0.7733
## F-statistic: 171.5 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH15" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -336.82 -81.91 22.00 82.37 222.96
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8256.868 36.727 224.82 < 0.0000000000000002 ***
## op_count 13.935 1.266 11.01 0.00000000000000755 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 133.1 on 49 degrees of freedom
## Multiple R-squared: 0.7121, Adjusted R-squared: 0.7062
## F-statistic: 121.2 on 1 and 49 DF, p-value: 0.000000000000007554
## [1] "PUSH16" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -274.69 -71.65 15.26 79.64 225.70
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8269.392 32.416 255.10 < 0.0000000000000002 ***
## op_count 13.203 1.117 11.82 0.000000000000000595 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 117.5 on 49 degrees of freedom
## Multiple R-squared: 0.7402, Adjusted R-squared: 0.7349
## F-statistic: 139.6 on 1 and 49 DF, p-value: 0.0000000000000005953
## [1] "PUSH17" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -288.012 -78.819 -7.902 72.041 249.472
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8309.496 34.158 243.27 < 0.0000000000000002 ***
## op_count 13.256 1.177 11.26 0.00000000000000341 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 123.8 on 49 degrees of freedom
## Multiple R-squared: 0.7212, Adjusted R-squared: 0.7155
## F-statistic: 126.8 on 1 and 49 DF, p-value: 0.000000000000003405
## [1] "PUSH18" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -271.15 -82.81 -10.41 72.04 288.14
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8268.409 35.732 231.40 < 0.0000000000000002 ***
## op_count 13.866 1.232 11.26 0.00000000000000341 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 129.5 on 49 degrees of freedom
## Multiple R-squared: 0.7212, Adjusted R-squared: 0.7155
## F-statistic: 126.8 on 1 and 49 DF, p-value: 0.000000000000003405
## [1] "PUSH19" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -302.09 -58.71 -21.16 86.67 289.45
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8235.746 33.400 246.58 < 0.0000000000000002 ***
## op_count 13.558 1.151 11.78 0.000000000000000673 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 121 on 49 degrees of freedom
## Multiple R-squared: 0.7389, Adjusted R-squared: 0.7336
## F-statistic: 138.7 on 1 and 49 DF, p-value: 0.0000000000000006733
## [1] "PUSH20" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -339.96 -69.86 -11.60 59.22 328.44
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8317.96 36.84 225.778 < 0.0000000000000002 ***
## op_count 11.51 1.27 9.061 0.00000000000479 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 133.5 on 49 degrees of freedom
## Multiple R-squared: 0.6262, Adjusted R-squared: 0.6186
## F-statistic: 82.1 on 1 and 49 DF, p-value: 0.000000000004788
## [1] "PUSH21" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -257.59 -90.18 -30.43 85.19 250.98
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8342.736 35.206 236.967 < 0.0000000000000002 ***
## op_count 10.238 1.214 8.437 0.0000000000414 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 127.6 on 49 degrees of freedom
## Multiple R-squared: 0.5923, Adjusted R-squared: 0.5839
## F-statistic: 71.18 on 1 and 49 DF, p-value: 0.00000000004139
## [1] "PUSH22" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -269.89 -108.94 0.94 112.49 323.95
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8315.249 39.258 211.8 < 0.0000000000000002 ***
## op_count 11.637 1.353 8.6 0.0000000000235 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 142.2 on 49 degrees of freedom
## Multiple R-squared: 0.6015, Adjusted R-squared: 0.5933
## F-statistic: 73.95 on 1 and 49 DF, p-value: 0.00000000002349
## [1] "PUSH23" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -285.01 -113.13 -3.25 110.10 417.47
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8245.019 37.011 222.77 < 0.0000000000000002 ***
## op_count 13.601 1.276 10.66 0.000000000000023 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 134.1 on 49 degrees of freedom
## Multiple R-squared: 0.6988, Adjusted R-squared: 0.6926
## F-statistic: 113.7 on 1 and 49 DF, p-value: 0.00000000000002301
## [1] "PUSH24" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -420.76 -82.62 -11.30 91.71 219.12
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8346.865 35.418 235.669 < 0.0000000000000002 ***
## op_count 10.498 1.221 8.599 0.0000000000235 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 128.3 on 49 degrees of freedom
## Multiple R-squared: 0.6014, Adjusted R-squared: 0.5933
## F-statistic: 73.95 on 1 and 49 DF, p-value: 0.00000000002352
## [1] "PUSH25" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -298.11 -77.41 15.76 74.63 234.09
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8297.583 33.751 245.848 < 0.0000000000000002 ***
## op_count 11.252 1.163 9.672 0.000000000000602 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 122.3 on 49 degrees of freedom
## Multiple R-squared: 0.6563, Adjusted R-squared: 0.6492
## F-statistic: 93.55 on 1 and 49 DF, p-value: 0.0000000000006021
## [1] "PUSH26" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -163.82 -63.96 -13.94 51.46 268.41
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8320.0255 26.5811 313.00 <0.0000000000000002 ***
## op_count 11.6498 0.9162 12.71 <0.0000000000000002 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 96.31 on 49 degrees of freedom
## Multiple R-squared: 0.7674, Adjusted R-squared: 0.7627
## F-statistic: 161.7 on 1 and 49 DF, p-value: < 0.00000000000000022
## [1] "PUSH27" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -361.89 -97.53 -10.52 95.76 273.08
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8251.996 35.362 233.36 < 0.0000000000000002 ***
## op_count 12.511 1.219 10.26 0.0000000000000841 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 128.1 on 49 degrees of freedom
## Multiple R-squared: 0.6825, Adjusted R-squared: 0.6761
## F-statistic: 105.4 on 1 and 49 DF, p-value: 0.00000000000008411
## [1] "PUSH28" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -257.59 -83.16 -2.43 63.12 333.21
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8223.598 33.634 244.50 < 0.0000000000000002 ***
## op_count 13.279 1.159 11.45 0.00000000000000184 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 121.9 on 49 degrees of freedom
## Multiple R-squared: 0.7281, Adjusted R-squared: 0.7225
## F-statistic: 131.2 on 1 and 49 DF, p-value: 0.00000000000000184
## [1] "PUSH29" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -212.40 -69.94 -18.42 80.10 299.92
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8324.013 32.810 253.700 < 0.0000000000000002 ***
## op_count 10.403 1.131 9.198 0.00000000000299 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 118.9 on 49 degrees of freedom
## Multiple R-squared: 0.6333, Adjusted R-squared: 0.6258
## F-statistic: 84.61 on 1 and 49 DF, p-value: 0.00000000000299
## [1] "PUSH30" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -309.398 -84.923 7.114 84.736 314.625
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8343.707 36.286 229.940 < 0.0000000000000002 ***
## op_count 9.111 1.251 7.284 0.00000000241 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 131.5 on 49 degrees of freedom
## Multiple R-squared: 0.5199, Adjusted R-squared: 0.5101
## F-statistic: 53.06 on 1 and 49 DF, p-value: 0.00000000241
## [1] "PUSH31" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -268.01 -80.44 -14.91 78.96 255.07
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8265.152 32.270 256.12 < 0.0000000000000002 ***
## op_count 11.755 1.112 10.57 0.0000000000000311 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 116.9 on 49 degrees of freedom
## Multiple R-squared: 0.6951, Adjusted R-squared: 0.6888
## F-statistic: 111.7 on 1 and 49 DF, p-value: 0.00000000000003113
## [1] "PUSH32" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -247.736 -78.847 -3.202 89.197 230.260
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 8302.457 34.828 238.382 < 0.0000000000000002 ***
## op_count 8.976 1.201 7.477 0.00000000121 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 126.2 on 49 degrees of freedom
## Multiple R-squared: 0.5329, Adjusted R-squared: 0.5234
## F-statistic: 55.91 on 1 and 49 DF, p-value: 0.000000001214
## [1] "DUP1" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -313.16 -113.22 -2.37 105.95 375.86
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10248.696 45.678 224.370 < 0.0000000000000002 ***
## op_count 7.889 1.574 5.011 0.00000746 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 165.5 on 49 degrees of freedom
## Multiple R-squared: 0.3388, Adjusted R-squared: 0.3253
## F-statistic: 25.11 on 1 and 49 DF, p-value: 0.000007457
## [1] "DUP2" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -384.59 -111.29 18.53 96.35 332.79
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10302.321 45.117 228.348 < 0.0000000000000002 ***
## op_count 5.890 1.555 3.788 0.000416 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 163.5 on 49 degrees of freedom
## Multiple R-squared: 0.2265, Adjusted R-squared: 0.2107
## F-statistic: 14.35 on 1 and 49 DF, p-value: 0.0004162
## [1] "DUP3" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -313.05 -99.21 0.50 77.26 460.88
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10279.964 43.312 237.350 < 0.0000000000000002 ***
## op_count 6.373 1.493 4.269 0.0000897 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 156.9 on 49 degrees of freedom
## Multiple R-squared: 0.2711, Adjusted R-squared: 0.2562
## F-statistic: 18.22 on 1 and 49 DF, p-value: 0.00008975
## [1] "DUP4" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -325.25 -86.31 -14.52 76.85 691.97
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10334.746 45.753 225.88 < 0.0000000000000002 ***
## op_count 5.141 1.577 3.26 0.00203 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 165.8 on 49 degrees of freedom
## Multiple R-squared: 0.1782, Adjusted R-squared: 0.1615
## F-statistic: 10.63 on 1 and 49 DF, p-value: 0.00203
## [1] "DUP5" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -374.57 -62.85 9.37 83.02 259.81
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10335.091 34.987 295.394 < 0.0000000000000002 ***
## op_count 4.331 1.206 3.591 0.000761 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 126.8 on 49 degrees of freedom
## Multiple R-squared: 0.2084, Adjusted R-squared: 0.1922
## F-statistic: 12.9 on 1 and 49 DF, p-value: 0.0007608
## [1] "DUP6" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -344.33 -68.87 6.56 82.39 208.23
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10288.759 31.122 330.60 < 0.0000000000000002 ***
## op_count 5.932 1.073 5.53 0.00000123 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 112.8 on 49 degrees of freedom
## Multiple R-squared: 0.3843, Adjusted R-squared: 0.3717
## F-statistic: 30.58 on 1 and 49 DF, p-value: 0.000001229
## [1] "DUP7" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -342.01 -109.75 14.84 95.44 459.36
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10259.668 43.179 237.609 < 0.0000000000000002 ***
## op_count 8.593 1.488 5.774 0.000000521 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 156.5 on 49 degrees of freedom
## Multiple R-squared: 0.4049, Adjusted R-squared: 0.3927
## F-statistic: 33.34 on 1 and 49 DF, p-value: 0.0000005214
## [1] "DUP8" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -482.62 -76.25 -20.25 110.35 293.19
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10262.537 42.842 239.546 < 0.0000000000000002 ***
## op_count 8.528 1.477 5.775 0.000000519 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 155.2 on 49 degrees of freedom
## Multiple R-squared: 0.405, Adjusted R-squared: 0.3928
## F-statistic: 33.35 on 1 and 49 DF, p-value: 0.000000519
## [1] "DUP9" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -287.363 -72.360 1.348 80.210 288.349
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10264.540 39.025 263.024 < 0.0000000000000002 ***
## op_count 6.487 1.345 4.822 0.0000142 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 141.4 on 49 degrees of freedom
## Multiple R-squared: 0.3218, Adjusted R-squared: 0.308
## F-statistic: 23.25 on 1 and 49 DF, p-value: 0.00001419
## [1] "DUP10" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -330.04 -121.50 17.40 76.47 360.99
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10326.012 43.432 237.750 < 0.0000000000000002 ***
## op_count 5.901 1.497 3.942 0.000257 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 157.4 on 49 degrees of freedom
## Multiple R-squared: 0.2407, Adjusted R-squared: 0.2252
## F-statistic: 15.54 on 1 and 49 DF, p-value: 0.000257
## [1] "DUP11" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -311.44 -84.31 -20.19 70.15 442.81
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10354.529 39.028 265.311 < 0.0000000000000002 ***
## op_count 5.869 1.345 4.362 0.0000661 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 141.4 on 49 degrees of freedom
## Multiple R-squared: 0.2797, Adjusted R-squared: 0.265
## F-statistic: 19.03 on 1 and 49 DF, p-value: 0.00006608
## [1] "DUP12" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -240.86 -114.45 0.72 83.38 326.98
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10257.632 39.920 256.956 < 0.0000000000000002 ***
## op_count 7.288 1.376 5.297 0.00000278 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 144.6 on 49 degrees of freedom
## Multiple R-squared: 0.3641, Adjusted R-squared: 0.3511
## F-statistic: 28.05 on 1 and 49 DF, p-value: 0.000002777
## [1] "DUP13" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -259.8 -116.2 -7.2 118.0 342.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10294.366 40.030 257.168 < 0.0000000000000002 ***
## op_count 6.746 1.380 4.889 0.0000113 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 145 on 49 degrees of freedom
## Multiple R-squared: 0.3279, Adjusted R-squared: 0.3142
## F-statistic: 23.9 on 1 and 49 DF, p-value: 0.0000113
## [1] "DUP14" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -289.38 -124.97 20.40 88.08 344.34
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10223.069 44.058 232.038 < 0.0000000000000002 ***
## op_count 9.603 1.519 6.323 0.0000000742 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 159.6 on 49 degrees of freedom
## Multiple R-squared: 0.4493, Adjusted R-squared: 0.4381
## F-statistic: 39.98 on 1 and 49 DF, p-value: 0.00000007415
## [1] "DUP15" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -319.41 -99.98 4.54 93.41 413.35
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10326.803 39.681 260.245 < 0.0000000000000002 ***
## op_count 6.787 1.368 4.962 0.00000881 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 143.8 on 49 degrees of freedom
## Multiple R-squared: 0.3344, Adjusted R-squared: 0.3208
## F-statistic: 24.62 on 1 and 49 DF, p-value: 0.000008813
## [1] "DUP16" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -334.05 -107.60 -18.68 82.71 389.77
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 10361.682 42.958 241.208 < 0.0000000000000002 ***
## op_count 8.156 1.481 5.508 0.00000133 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 155.7 on 49 degrees of freedom
## Multiple R-squared: 0.3824, Adjusted R-squared: 0.3698
## F-statistic: 30.34 on 1 and 49 DF, p-value: 0.000001326
## [1] "SWAP1" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -373.39 -119.48 -15.61 96.11 352.28
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9342.108 46.220 202.122 < 0.0000000000000002 ***
## op_count 7.282 1.593 4.571 0.0000331 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 167.5 on 49 degrees of freedom
## Multiple R-squared: 0.2989, Adjusted R-squared: 0.2846
## F-statistic: 20.89 on 1 and 49 DF, p-value: 0.00003308
## [1] "SWAP2" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -243.476 -63.332 -3.653 59.072 285.431
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9239.467 29.617 311.962 < 0.0000000000000002 ***
## op_count 9.778 1.021 9.578 0.000000000000825 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 107.3 on 49 degrees of freedom
## Multiple R-squared: 0.6518, Adjusted R-squared: 0.6447
## F-statistic: 91.74 on 1 and 49 DF, p-value: 0.000000000000825
## [1] "SWAP3" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -231.61 -79.96 -1.78 79.02 324.10
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9256.748 34.945 264.898 < 0.0000000000000002 ***
## op_count 9.520 1.205 7.904 0.000000000268 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 126.6 on 49 degrees of freedom
## Multiple R-squared: 0.5604, Adjusted R-squared: 0.5514
## F-statistic: 62.47 on 1 and 49 DF, p-value: 0.0000000002683
## [1] "SWAP4" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -296.76 -70.67 -16.15 86.24 257.94
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9288.778 33.539 276.958 < 0.0000000000000002 ***
## op_count 9.041 1.156 7.821 0.000000000359 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 121.5 on 49 degrees of freedom
## Multiple R-squared: 0.5552, Adjusted R-squared: 0.5461
## F-statistic: 61.17 on 1 and 49 DF, p-value: 0.0000000003594
## [1] "SWAP5" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -314.68 -87.68 -22.22 114.18 262.48
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9325.489 35.013 266.347 < 0.0000000000000002 ***
## op_count 6.663 1.207 5.521 0.00000127 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 126.9 on 49 degrees of freedom
## Multiple R-squared: 0.3835, Adjusted R-squared: 0.3709
## F-statistic: 30.48 on 1 and 49 DF, p-value: 0.000001267
## [1] "SWAP6" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -248.97 -91.41 1.25 89.26 390.54
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9350.897 37.800 247.380 < 0.0000000000000002 ***
## op_count 8.279 1.303 6.354 0.0000000665 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 137 on 49 degrees of freedom
## Multiple R-squared: 0.4517, Adjusted R-squared: 0.4405
## F-statistic: 40.37 on 1 and 49 DF, p-value: 0.00000006651
## [1] "SWAP7" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -206.680 -70.638 -1.938 80.314 246.966
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9295.734 30.249 307.31 < 0.0000000000000002 ***
## op_count 10.659 1.043 10.22 0.0000000000000962 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 109.6 on 49 degrees of freedom
## Multiple R-squared: 0.6808, Adjusted R-squared: 0.6743
## F-statistic: 104.5 on 1 and 49 DF, p-value: 0.00000000000009623
## [1] "SWAP8" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -269.96 -78.03 -15.70 52.70 444.26
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9273.866 37.367 248.183 < 0.0000000000000002 ***
## op_count 9.173 1.288 7.122 0.0000000043 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 135.4 on 49 degrees of freedom
## Multiple R-squared: 0.5086, Adjusted R-squared: 0.4986
## F-statistic: 50.72 on 1 and 49 DF, p-value: 0.000000004301
## [1] "SWAP9" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -221.296 -71.806 8.203 55.715 188.522
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9323.7892 26.2656 354.98 < 0.0000000000000002 ***
## op_count 8.1571 0.9054 9.01 0.0000000000057 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 95.17 on 49 degrees of freedom
## Multiple R-squared: 0.6236, Adjusted R-squared: 0.6159
## F-statistic: 81.18 on 1 and 49 DF, p-value: 0.000000000005698
## [1] "SWAP10" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -193.44 -69.82 -15.66 79.56 271.78
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9351.937 32.109 291.252 < 0.0000000000000002 ***
## op_count 10.035 1.107 9.067 0.00000000000469 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 116.3 on 49 degrees of freedom
## Multiple R-squared: 0.6265, Adjusted R-squared: 0.6189
## F-statistic: 82.2 on 1 and 49 DF, p-value: 0.000000000004693
## [1] "SWAP11" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -325.75 -82.67 -12.84 63.57 422.65
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9272.956 36.479 254.198 < 0.0000000000000002 ***
## op_count 8.959 1.257 7.125 0.00000000424 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 132.2 on 49 degrees of freedom
## Multiple R-squared: 0.5089, Adjusted R-squared: 0.4988
## F-statistic: 50.77 on 1 and 49 DF, p-value: 0.000000004244
## [1] "SWAP12" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -272.06 -108.33 -2.88 80.19 421.82
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9329.727 41.852 222.921 < 0.0000000000000002 ***
## op_count 9.617 1.443 6.667 0.0000000218 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 151.6 on 49 degrees of freedom
## Multiple R-squared: 0.4756, Adjusted R-squared: 0.4649
## F-statistic: 44.44 on 1 and 49 DF, p-value: 0.0000000218
## [1] "SWAP13" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -425.84 -102.35 6.37 96.81 411.15
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9329.302 44.484 209.723 < 0.0000000000000002 ***
## op_count 8.333 1.533 5.435 0.00000171 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 161.2 on 49 degrees of freedom
## Multiple R-squared: 0.3761, Adjusted R-squared: 0.3634
## F-statistic: 29.54 on 1 and 49 DF, p-value: 0.000001715
## [1] "SWAP14" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -251.00 -90.91 -1.72 90.93 332.90
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9317.011 37.826 246.314 < 0.0000000000000002 ***
## op_count 7.631 1.304 5.853 0.000000395 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 137.1 on 49 degrees of freedom
## Multiple R-squared: 0.4114, Adjusted R-squared: 0.3994
## F-statistic: 34.25 on 1 and 49 DF, p-value: 0.0000003946
## [1] "SWAP15" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -223.80 -98.53 -13.86 91.71 499.27
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9415.040 37.526 250.897 < 0.0000000000000002 ***
## op_count 8.596 1.293 6.646 0.0000000235 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 136 on 49 degrees of freedom
## Multiple R-squared: 0.474, Adjusted R-squared: 0.4633
## F-statistic: 44.16 on 1 and 49 DF, p-value: 0.0000000235
## [1] "SWAP16" "nethermind"
##
## Call:
## lm(formula = measure_total_time_ns ~ op_count, data = df_mean)
##
## Residuals:
## Min 1Q Median 3Q Max
## -276.895 -91.191 9.917 77.807 262.051
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9375.487 36.399 257.577 < 0.0000000000000002 ***
## op_count 9.437 1.255 7.522 0.00000000104 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 131.9 on 49 degrees of freedom
## Multiple R-squared: 0.5359, Adjusted R-squared: 0.5264
## F-statistic: 56.58 on 1 and 49 DF, p-value: 0.000000001036
Export the results
write.csv(estimates, paste0("../../local/", env, "_marginal_estimated_cost.csv"), quote=FALSE, row.names=FALSE)